Data & Analytics

Experian’s newest Global Insights Report found that consumers are online 25% more today than they were just a year ago, highlighting the importance of the digital customer experience. To acquire customers and retain their loyalty, businesses need to focus on improving the online experience, preventing fraud, and managing credit risk.   This September, Experian surveyed 3,000 consumers and 900 businesses across all industries to explore business priorities and recent changes in consumer activities.   Many businesses and consumers are reportedly feeling more economically stable now than they were a year ago. As consumers resume spending the digital customer experience becomes even more paramount – requiring businesses to invest in scalable software solutions that will accurately assess credit risk and meet ever-changing needs and priorities.   Our research found that:   42% of consumers have increased concern for the safety of banking and shopping transactions Business adoption of advanced analytics has increased over last year, and adoption of artificial intelligence is up from 69% to 74% Consumers are more likely to share their personal data if it improves their experience, with 56% willing to share their contact information The top three consumer priorities continue to be security, privacy and convenience   Download the report to get all the latest insights into consumer desires and business behaviors as we move further through the digital evolution. Download the report

Chatbots, reduction of manual processes and explainability were all hot topics in a recent discussion between Madhurima Khandelwal, Vice President and Head of DataLabs at American Express®, and Eric Haller, Executive Vice President and head of Experian DataLabs. The importance of AI’s role in innovation in the financial services space was the focus of the recent video interview. In the interview, Khandelwal highlighted some of the latest in what American Express DataLabs is working on to continue to solve complex challenges by building tools driven by AI and Machine Learning: Natural language processing has come a long way in even the last few years. Khandelwal discussed how chat bots and conversational AI can automate the simple to complex to enhance customer experience. Document recognition and processing is another leading-edge innovation that is useful for extracting and analyzing information, which saves staff countless manual hours, Khandelwal said. Fairness and explainability are consistently brought to the forefront especially in financial services as regulators are looking at ways to prevent AI/ML from causing bias for the consumer. Khandelwal showcased how there is extreme rigor in each part of creating their models and how human oversight and training are primary drivers for how they stay on top of this. As for innovation advice, Khandelwal points out that it’s important to be aware that AI and innovation are not always interchangeable, and companies need to think through whether a problem needs to be solved through AI/ML models before charting ahead. Another major key to the equation is the data. In all use cases, the undercurrent of innovation in any form is dependent on the data being used. Learn more about this topic and what Harry Potter has to do with women in data science. Watch the Interview

It’s time for organizations to harness the power artificial intelligence (AI) can bring to digital identity management – quickly and accurately identifying consumers throughout the lifecycle. The rise in crime   The acceleration to digital platforms created a perfect storm of new opportunities for fraudsters. Synthetic identity fraud, stimulus-related fraud, and other types of cybercrime have seen huge upticks within the past year and a half. In fact, the Federal Trade Commission revealed that consumers reported over 360,000 complaints, resulting in more than $580 million in COVID-19-related fraud losses as of October 2021. To protect both themselves and consumers, businesses — especially lenders — will have to find and incorporate new strategies to identify customers, deter fraudsters and mitigate cybercrime. The benefits of AI for digital identity In our latest e-book, we explore the impacts of AI on organizations’ digital identity strategies, including: How changing consumer expectations increased the need for speed The challenges associated with both AI and digital identities The path forward for digital identity and AI How to develop the right strategy Building a solution It’s clear that current digital identity and fraud prevention tools are not enough to stop cybercriminals. To stay ahead of fraudsters and keep consumers happy, businesses need to look to new technologies — ones that can intake and compute large data sets in near-real time for better and faster decisions throughout the customer lifecycle. By using AI, businesses will enjoy a fast and consistent decisioning system that automatically routes questionable identities to additional authentication steps, allowing employees to focus on the riskiest cases and maximizing efficiency. Read our latest e-book to dive into the ways artificial intelligence and digital identity interact, and the benefits a clear identity strategy can have for the entire user journey. Download the e-book

Shri Santhanam, Executive Vice President and General Manager of Global Analytics and Artificial Intelligence (AI) was recently featured on Lendit’s ‘Fintech One-on-One’ podcast. Shri and podcast creator, Peter Renton, discussed advanced analytics and AI’s role in lending and how Experian is helping lenders during what he calls the ‘digital lending revolution.’ Digital lending revolution “Over the last decade and a half, the notion of digital tools, decisioning, analytics and underwriting has come into play. The COVID-19 pandemic has dramatically accelerated that, and we’re seeing three big trends shake up the financial services industry,” said Shri. A shift in consumer expectations More than ever before, there is a deep focus on the customer experience. Five or six years ago, consumers and businesses were more accepting of waiting several days, sometimes even weeks, for loan approvals and decisions. However, the expectation has dramatically changed. In today’s digital world, consumers expect lending institutions to make quick approvals and real-time decisions. Fintechs being quick to act Fintech lenders have been disrupting the traditional financial services space in ways that positively impacts consumers. They’ve made it easier for borrowers to access credit – particularly those who have been traditional excluded or denied – and are quick to identify, develop and distribute market solutions. An increased adoption of machine learning, advanced analytics and AI Fintechs and financial institutions of all sizes are further exploring using AI-powered solutions to unlock growth and improve operational efficiencies. AI-driven strategies, which were once a ‘nice-to-have,’ have become a necessity. To help organizations reduce the resources and costs associated with building in-house models, Experian has launched Ascend Intelligence Services™, an analytics solution delivered on a modern tech AI platform. Ascend Intelligence Services helps streamline model builds and increases decision automation and approval rates. The future of lending: will all lending be done via AI, and what will it take to get there? According to Shri, lending in AI is inevitable. The biggest challenge the lending industry may face is trust in advanced analytics and AI decisioning to ensure lending is fair and transparent. Can AI-based lending help solve for biases in credit decisioning? We believe so, with the right frameworks and rules in place. Want to learn more? Explore our fintech solutions or click below. Listen to Podcast Learn more about Ascend Intelligence Services

Despite used vehicle prices rising, loan-to-value (LTVs) ratios are dropping. Ultimately, lower LTVs are a positive trend for consumers because it puts them into positive equity on their vehicle faster.

Experian recently announced that it has made the IDC 2021 Fintech Rankings Top 100, highlighting the best global providers of financial technology. Experian is ranked number 11, rising 33 places from its 2020 ranking. IDC also refers to Experian as a ‘rising star.’ The robust data assets of Experian, combined with best-in-class modeling, decisioning and technology are powering new and innovative solutions. Experian has invested heavily in new technologies and infrastructures to deliver the freshest insights at the right time, to make the best decision. For example, Experian's Ascend Intelligence Services™ provides data, analytics, strategy, and performance monitoring, delivered on a modern-tech AI platform. With the investment in Ascend Intelligence Services, Experian has been able to streamline the delivery speed of analytical solutions to clients, improve decision automation rates and increase approval rates, in some cases by double digits. “Recognition in the top 20 of IDC FinTech Rankings demonstrates Experian’s commitment to the success of its financial clients,” said Marc DeCastro, research director at IDC Financial Insights. “We congratulate Experian for being ranked 11th in the 2021 IDC FinTech Rankings Top 100 list.” View the IDC Fintech Rankings list in its entirety here. Focus on Data, Advanced Analytics and Decisioning Creates Winning Strategy for Experian Experian’s focus on data, advanced analytics and decisioning has continued to gain recognition from various notable programs that acknowledge Fintech industry leaders and breakthrough technologies worldwide. Beyond the IDC Fintech Rankings Top 100, Experian won honors from the 2021 FinTech Breakthrough Awards, the 2021 CIO 100 Awards and was most recently shortlisted in the CeFPro Global Fintech Leaders List for 2022 in the categories of advanced analytics, anti-fraud, credit risk and core banking/back-end system technologies. “At Experian, we are committed to supporting the Fintech community. It’s great to see our continued efforts and investments driving positive impacts for our clients and their consumers. We will continue to invest and innovate to help our clients solve problems, create opportunities and support their customer-first missions,” said Jon Bailey, Vice President for Fintech at Experian. Learn more about how Experian can help advance your business goals with our Fintech Solutions and Ascend Intelligence Services. Explore fintech solutions Learn more about AIS

Financial inclusion is a challenge, that, while not new, has become ever more apparent over the last year. The inequities and inequalities in our society, exasperated by the COVID-19 pandemic, which disproportionately affected underserved populations, have amplified the challenge lenders and others in the financial services industry face in fostering financial inclusion. As a result, there is an increased focus and importance on diversity, equity and inclusion (DEI) and having the ability to assess creditworthiness of overlooked and ‘invisible’ consumers. In a recent webinar, we sat down with Sarah Davies, Head of Data Analytics at Nova Credit, and a panel of Experian experts including Wil Lewis, Chief Diversity, Equity and Inclusion Officer, Alpa Lally, Vice President of Product Management, and Greg Wright, Product Chief Officer, to explore the topic of DEI, what best practices exist to break down financial inclusion barriers and move financial access forward for all, and real-takeaway strategies and capabilities designed for fintechs and other financial institutions to leverage for lending deeper. Below are a few key perspectives from our speakers: What barriers to access are there for credit across different groups of people? [WL]: There are many barriers to financial inclusion, especially for underserved communities. The first of which is lack of awareness and lack of education about credit and how it impacts financial access – from obtaining loans, buying a first home, a new car and more. Not every American has someone in their life to teach and provide coaching on credit responsibility and how to be financially literate. [AL]: Historically, credit, wealth and health inequalities have all contributed to financial disparities, and as a result, have created an underrepresentation of marginalized communities in the current credit ecosystem. That’s compounded by today’s ecosystem where consumer underwriting favors those with established thick-file credit histories with minimal delinquencies, particularly in the last 24 months. So, all things being equal, additional points distributed to elevate scores are given to consumers that are maintaining low revolving debt. This poses credit barriers for those starting out new to credit, or even to immigrants coming into a new country who don’t have an established credit history.   What role could the credit industry play in healing the financial disparities created by the COVID-19 pandemic? [WL]: Our opportunity lies in meeting consumers where they are today. COVID-19 has spotlighted economic and social disparities in a way it hasn’t been done before. At the same time, it illustrated how the inability of some groups to access financial services requires meaningful solutions, quickly. Historically, organizations have been known to look at this in a traditional way: meaning “we are the organization, consumers come to us and we can tell you what you can and can’t do.” We need to shift our focus to how we can provide consumers with tools, technology and machine learning (ML) that are available to empower them. [SD]: One of the lessons we’ve learned from COVID, is that we need to be able to get to the marketplace fast in order to respond to the economic conditions. Fintechs have been very effective at this and it has shown through the approach they’ve taken towards immediacy in identifying, developing and distributing solutions. With consumers in a stressed position, it’s incumbent upon us, as the industry, to deliver consumer-centric options and opportunities in an efficient manner rather than having our consumers sit around waiting for them.   Are there solutions to help ensure we are lending deeper and serving thin-filed consumers? [AL]: At its core, data – not limited to only traditional credit data – that can be decisioned on, can help enrich financial inclusion. Alternative data, or expanded FCRA data, means that the data is displayable, disputable and correctable by the consumer. We recognize that traditional credit is still an effective way to assess a consumer’s credit worthiness. However, expanded FCRA data includes data points from rental, video streaming, all other industry sectors to help provide a 360 view of the consumer with additional insights - whether you are a thick-file consumer, thin-file consumer, or credit invisible. Through these different various data assets paired with advanced analytics and ML, we now have a mechanism to make sure consumers go from credit invisible to visible – and scorable. Leveraging Experian Boost and Experian Lift scores can do just that. [SD]: Expanded FCRA data is powerful and vital for helping the consumer. In addition, we are now in a place where the consumer can take on the responsibility and accountability for giving permission to include their data in the credit score. You’re putting the consumers in the driver seat, and with that, we are dissolving the psychological barriers that consumers may have had previously around their credit score being out of their control. As a player in the financial services space, we can put out as much data as we want, but it’s about engaging the consumer, sharing with them how it’s safe to share their data, and what the benefits of doing so are.   Are there tangible and intangible benefits of DEI that companies can realize when they have formal DEI programs in place? [WL]: Often times, when we think of lending, we talk about it from the standpoint of our business – ‘what are we doing for our customers, how are we helping consumers who are going to a institution for a loan.’ What we typically forget about is our own backyard. Every organization has employees who are at different points in their credit journey. How often do we talk directly to our employees and give them tools and details that may help them, their family member, or neighbor? As I think about DEI, it’s about involving folks inside your company to continue moving financial inclusion forward. As for an intangible benefit, when doing work in DEI and driving impact, you’re also reducing negative reputational risk. Reputable brands are invaluable, as you begin to make and show an impact, consumers begin to trust you. [AL]: Brand and reputation is huge in today’s world. We are starting to see a shift in consumers selecting certain institutions to work with, not just because of the services provided, but because it’s based on the brand and what they stand for. You as an institution are doing financial inclusion and you’re living up to it. You are truly embarking internally and externally on this initiative and it adds weights on the products and solutions that you sell. For consumers, that may be very important.   What does the future look like relative to financial inclusion? [WL]: It’s a world where all of us play a role in - no matter where you are in the organization. It’s all of our jobs and responsibility to talk about it to our fellow neighbor, consumer, and direct them to tools that will help them. [SD]: We no longer need to justify why financial inclusion is necessary. We’ve got all the data we need. Tools and mechanisms for organizations and consumers are almost universally available. The go-forward view requires all ‘players’ within the space to aggressively embrace these tools and data and start sharing and applying them across all markets and verticals. There’s no longer a reason not to be able to underwrite somebody with a thin file or marginal set of data. We have everything in place at this point. [AL]: It’s all our jobs. I think we have to put a lot of importance on our younger leaders and colleagues to carry our initiatives forward, so we are truly inclusive. We have just started taking the initial steps and we’ve made good progress, but we need to continue to make progress. In the future, I hope to see all that are younger take this forward and drive financial inclusion for all across the spectrum. Watch the full session to hear more of the engaging and timely discussion. Access the recording To learn more about how Experian is committed to advancing financial inclusion, please visit Experian’s Inclusion Forward resources page. For Fintechs looking to partner with Experian on marketplace lending solutions, explore our solutions here.

Artificial intelligence is here to stay, and businesses who are adopting the newest AI technology are ahead of the game. From targeting the right prospects to designing effective collections efforts, AI-driven strategies across the entire customer lifecycle are no longer a nice to have - they are a must.  Many organizations are late to the game of AI and/or are spending too much time and money designing and redesigning models and deploying them over weeks and months. By the time these models are deployed, markets may have already shifted again, forcing strategy teams to go back to the drawing board. And if these models and strategies are not being continuously monitored, they can become less effective over time and lead to missed opportunities and lost revenue. By implementing artificial intelligence in predictive modeling and strategy optimization, financial institutions and lenders can design and deploy their decisioning strategies faster than ever before and make incremental changes on the fly to adapt to evolving market trends.  While most organizations say they want to incorporate artificial intelligence and machine learning into their business strategy, many do not know where to start. Targeting, portfolio management, and collections are some of the top use cases for AI/ML strategy initiatives.  Targeting  One way businesses are using AI-driven modeling is for targeting the audiences that will most likely meet their credit criteria and respond to their offers. Financial institutions need to have the right data to inform a decisioning strategy that recognizes credit criteria, can respond immediately when prospects meet that criteria and can be adjusted quickly when those factors change. AI-driven response models and optimized decision strategies perform these functions seamlessly, giving businesses the advantage of targeting the right prospects at the right time.  Credit portfolio management  Risk models optimized with artificial intelligence and machine learning, built on comprehensive data sets, are being used by credit lenders to acquire new revenue and set appropriate balance limits. Strategies built around AI-driven risk models enable businesses to send new offers and cross-sell offers to current customers, while appropriately setting initial credit limits and managing limits over time for increased wallet share and reduced risk.   Collections  AI- and ML-driven analytics models are also optimizing collections strategies to improve recovery rates. Employing AI-powered balance and response models, credit lenders can make smarter collections decisions based on the most predictive and accurate information available.   For lending businesses who are already tight on resources, or those whose IT teams cannot meet the demand of quickly adapting to ever-changing market conditions and decisioning criteria, a managed service for AI-powered models and strategy design might be the best option. Managed service teams work closely with businesses to determine specific use cases, develop models to meet those use cases, deploy models quickly, and monitor models to ensure they keep producing and predicting optimally.  Experian offers Ascend Intelligence Services, the only managed service solution to provide data, analytics, strategy and performance monitoring. Experian’s data scientists provide expert guidance as they collaborate with businesses in developing and deploying models and strategies around targeting, acquisitions, limit-setting, and collections. Once those strategies are deployed, Experian continually monitors model health to ensure scores are still predictive and presents challenger models so credit lenders can always have the most accurate decisioning models for their business. Ascend Intelligence Services provides an online dashboard for easy visibility, documentation for regulatory compliance, and cloud capabilities to deliver scores and decisions in real-time.  Experian’s Ascend Intelligence Services makes getting into the AI game easy. Start realizing the power of data and AI-driven analytics models by using our ROI calculator below: initIframe('611ea3adb1ab9f5149cf694e'); For more information about Ascend Intelligence Services, visit our webpage or join our upcoming webinar on October 21, 2021.  Learn more Register for webinar

The automotive finance market is beginning to level out to pre-pandemic trends in Q2 2021.

The collections landscape is changing as a result of new and upcoming legislation and increased expectations from consumers. Because of this, businesses are looking to create more effective, consumer-focused collections processes while remaining within regulatory guidelines. Our latest tip sheet has insights that can help businesses and agencies optimize their collections efforts and remain compliant, including:   Start with the best data Keep pace with changing regulations Focus on agility Pick the right partner Download the tip sheet to learn how to maximize your collections efforts while reducing costs, avoiding reputational damage and fines, and improving overall engagement. Download tip sheet

Millions of consumers lack credit history and/or have difficulty obtaining credit from mainstream financial institutions. As a result, the use of expanded Fair Credit Reporting Act (FCRA) – or alternative – data has continued to gain popularity among lenders and financial intuitions to enrich decisions across the entire lending lifecycle to meet the financial needs of their consumers. Experian presented in a recent webinar hosted by AFSA, where Alpa Lally, Vice President of Product Management, and David Elmore, Automotive Solutions Consultant, had a chance to speak about the benefits of FCRA data, and ways lenders can leverage this data to ease access to credit for “invisible” and below prime consumers. Watch the full webinar, “FCRA Data: The Key to Unlocking Credit Universe” and learn more about: How expanded FCRA data is being used throughout the lending lifecycle The benefits of leveraging FCRA data including providing a more holistic view of a consumer’s credit profile and behavior beyond financial services, leading to smarter, more informed lending decisions The lift FCRA data can offer when augmented with traditional credit data This webinar is a part of AFSA’s partner webinar series. To learn more about FCRA data and explore related content, please visit our FCRA Alternative Credit Data Resources Page. Learn More About FCRA-Alternative Credit Data

Lately, I’ve been surprised by the emphasis that some fraud prevention practitioners still place on manual fraud reviews and treatment. With the market’s intense focus on real-time decisions and customer experience, it seems that fraud processing isn’t always keeping up with the trends. I’ve been involved in several lively discussions on this topic. On one side of the argument sit the analytical experts who are incredibly good at distilling mountains of detailed information into the most accurate fraud risk prediction possible. Their work is intended to relieve users from the burden of scrutinizing all of that data. On the other side of the argument sits the human side of the debate. Their position is that only a human being is able to balance the complexity of judging risk with the sensitivity of handling a potential customer. All of this has led me to consider the pros and cons of manual fraud reviews. The Pros of Manual Review When we consider the requirements for review, it certainly seems that there could be a strong case for using a manual process rather than artificial intelligence. Human beings can bring knowledge and experience that is outside of the data that an analytical decision can see. Knowing what type of product or service the customer is asking for and whether or not it’s attractive to criminals leaps to mind. Or perhaps the customer is part of a small community where they’re known to the institution through other types of relationships—like a credit union with a community- or employer-based field of membership. In cases like these, there are valuable insights that come from the reviewer’s knowledge of the world outside of the data that’s available for analytics. The Cons of Manual Review When we look at the cons of manual fraud review, there’s a lot to consider. First, the costs can be high. This goes beyond the dollars paid to people who handle the review to the good customers that are lost because of delays and friction that occurs as part of the review process. In a past webinar, we asked approximately 150 practitioners how often an application flagged for identity discrepancies resulted in that application being abandoned. Half of the audience indicated that more than 50% of those customers were lost. Another 30% didn’t know what the impact was. Those potentially good customers were lost because the manual review process took too long. Additionally, the results are subjective. Two reviewers with different levels of skill and expertise could look at the same information and choose a different course of action or make a different decision. A single reviewer can be inconsistent, too—especially if they’re expected to meet productivity measures. Finally, manual fraud review doesn’t support policy development. In another webinar earlier this year, a fraud prevention practitioner mentioned that her organization’s past reliance on manual review left them unable to review fraud cases and figure out how the criminals were able to succeed. Her organization simply couldn’t recreate the reviewer’s thought process and find the mistake that lead to a fraud loss. To Review or Not to Review? With compelling arguments on both sides, what is the best practice for manually reviewing cases of fraud risk? Hopefully, the following list will help: DO: Get comfortable with what analytics tell you. Analytics divide events into groups that share a measurable level of fraud risk. Use the analytics to define different tiers of risk and assign each tier to a set of next steps. Start simple, breaking the accounts that need scrutiny into high, medium and low risk groups. Perhaps the high risk group includes one instance of fraud out of every five cases. Have a plan for how these will be handled. You might require additional identity documentation that would be hard for a criminal to falsify or some other action. Another group might include one instance in every 20 cases. A less burdensome treatment can be used here – like a one-time-passcode (OTP) sent to a confirmed mobile number. Any cases that remain unverified might then be asked for the same verification you used on the high-risk group. DON’T: Rely on a single analytical score threshold or risk indicator to create one giant pile of work that has to be sorted out manually. This approach usually results in a poor experience for a large number of customers, and a strong possibility that the next steps are not aligned to the level of risk. DO: Reserve manual review for situations where the reviewer can bring some new information or knowledge to the cases they review. DON’T: Use the same underlying data that generated the analytics as the basis of a review. Consider two simplistic cases that use a new address with no past association to the individual. In one case, there are several other people with different surnames that have recently been using the same address. In the other, there are only two, and they share the same surname. In the best possible case, the reviewer recognizes how the other information affects the risk, and they duplicate what the analytics have already done – flagging the first application as suspicious. In other cases, connections will be missed, resulting in a costly mistake. In real situations, automated reviews are able to compare each piece of information to thousands of others, making it more likely that second-guessing the analytics using the same data will be problematic. DO: Focus your most experienced and talented reviewers on creating fraud strategies. The best way to use their time and skill is to create a cycle where risk groups are defined (using analytics), a verification treatment is prescribed and used consistently, and the results are measured. With this approach, the outcome of every case is the result of deliberate action. When fraud occurs, it’s either because the case was miscategorized and received treatment that was too easy to discourage the criminal—or it was categorized correctly and the treatment wasn’t challenging enough. Gaining Value While there is a middle ground where manual review and skill can be a force-multiplier for strong analytics, my sense is that many organizations aren’t getting the best value from their most talented fraud practitioners. To improve this, businesses can start by understanding how analytics can help group customers based on levels of risk—not just one group but a few—where the number of good vs. fraudulent cases are understood. Decide how you want to handle each of those groups and reserve challenging treatments for the riskiest groups while applying easier treatments when the number of good customers per fraud attempt is very high. Set up a consistent waterfall process where customers either successfully verify, cascade to a more challenging treatment, or abandon the process. Focus your manual efforts on monitoring the process you’ve put in place. Start collecting data that shows you how both good and bad cases flow through the process. Know what types of challenges the bad guys are outsmarting so you can route them to challenges that they won’t beat so easily. Most importantly, have a plan and be consistent. Be sure to keep an eye out for a new post where we’ll talk about how this analytical approach can also help you grow your business. Contact us

Earlier this year, we shared our predictions for five fraud threats facing businesses in 2021. Now that we’ve reached the midpoint of the year and economic recovery is underway, we’re taking another look at how these threats can impact businesses and consumers.   Putting a Face to Frankenstein IDs: Synthetic identity fraudsters will attempt to bypass fraud detection methods by using AI to combine facial characteristics from different people to form a new identity. Overexposure: As many as 80% of SSNs may have been exposed on the dark web, creating opportunities for account application fraud. The Heist: Surges in data breaches, advances in automation, expanded online banking services and vulnerabilities exposed from social engineering mistakes have lead to rises in account takeover fraud. Overstimulated: Opportunistic fraudsters may take advantage of ongoing relief payments by using stolen data from consumers. Behind the Times: Businesses with lackluster fraud prevention tools and insufficient online security technology will likely experience more attacks and suffer larger losses.   To learn more about upcoming fraud threats and how to protect your business, download our new infographic and check out Experian’s fraud prevention solutions. Download infographic Request a call

Establishing a strong digital strategy remains a top priority for most financial institutions. With more eyes on screens and electronic devices, the pandemic-induced shift to digital has increased the need to meet consumers where they are. New Innovations As a Result of an Accelerated Shift to Digital  In Ernst & Young’s 2019 biannual Global Fintech Adoption Index, 46% of American respondents indicated they were using at least one fintech service. Fast forward, COVID-19 has accelerated the American adoption rate to 59%, according to a September survey conducted by Plaid, a leading digital payments infrastructure company. This shift to digital also resulted in an uptick in the creation of banking and savings processes that leverage advanced technologies. For example, digital-first technologies and artificial intelligence (AI) are changing the prescreen landscape as never before. For financial institutions, smart prescreen marketing solutions, coupled with a traditional approach to personalized service, present vast opportunities to build deeper consumer relationships. However, implementing an effective strategy can be challenging. In a recent webinar, Experian’s Vice President of Product Management Jacob Kong tackled the topic of using new analytics and AI to create a digital-first strategy. Joined by Mark Sievewright, founder of Sievewright & Associates and co-author of Digital Life, and Devon Kinkead, CEO of Micronotes.ai, they explored the evolution of banking and the possibilities offered by pairing data with technology in our new digital world. Watch the full webinar, 'Digital-First Strategies: New Analytics and Artificial Intelligence for Marketing,' and learn more about: The shift to digital life and banking, new analytics and AI How data and information value empowers prescreen marketing Emerging technologies and new tools that can support aggressive growth and marketing initiatives while mitigating risk How Experian is joining forces with Micronotes.ai to launch Micronotes ReFI powered by Experian, to help lower customers’ or members’ borrowing costs by refinancing mispriced debt Learn more about Micronotes ReFI powered by Experian To explore how Experian’s solutions and capabilities can power your prescreen and marketing strategies, please visit our solutions page or contact us for more information. Contact Us

Over the past year and a half, the development of digital identity has shifted the ways businesses interact with consumers. Companies across every industry have incorporated digital services, biometrics, and other verification tools to enhance the consumer experience without increasing risk.   Changing consumer expectations   A digital identity strategy is no longer a nice-to-have, it’s table stakes. Consumers expect to be recognized across platforms and have a seamless experience every time.   89% of consumers use mobile banking 80% of companies now have a customer recognition strategy in place 55% of banking customers say they plan to visit the bank branch less often moving forward   Businesses are responding to these changing expectations while working to grow during the economic recovery – trying to balance consumer experience with risk appetite and bottom-line goals. The present state of digital identity   Digital identity strategies require both standardization and interoperability. The first provides the ability to consistently capture data and characteristics that can be used to recognize a specific individual. The second allows businesses to resolve an identity to a specific person – recognizing a phone number, user ID and password, or a device – and use that information to determine if the user of the identity is in fact the identity owner.   There are some roadblocks on the road to a seamless digital identity strategy. Issues include a lack of consumer trust and an ambiguous regulatory landscape – creating friction on both ends of the equation.   Recipe for success   To succeed, businesses need a framework that can reliably use different combinations of physical and digital identity data to determine that the person behind the identity is a known, verified, and unique individual. A one-size-fits-all solution doesn’t exist. However, a layered approach allows businesses to modernize identity, providing the services consumers want and expect while remaining agile in an ever-changing environment.   In our newest white paper, developed in partnership with One World Identity, we explore the obstacles hindering digital identity management, and the best way to build a layered solution that is flexible, trustworthy, and inclusive.   To learn more, download our “Capturing the Digital Evolution Through a Layered Approach” white paper. Download white paper